Cluster-Based Bandits: Fast Cold-Start for Recommender System New Users

Abstract

How to quickly and reliably learn the preferences of new users remains a key challenge in the design of recommender systems. In this paper we introduce a new type of online learning algorithm, cluster-based bandits, to address this challenge. This exploits the fact that users can often be grouped into clusters based on the similarity of their preferences, and this allows accelerated learning of new user preferences since the task becomes one of identifying which cluster a user belongs to and typically there are far fewer clusters than there are items to be rated. Clustering by itself is not enough however. Intra-cluster variability between users can be thought of as adding noise to user ratings. Deterministic methods such as decision-trees perform poorly in the presence of such noise. We identify so-called distinguisher items that are particularly informative for deciding which cluster a new user belongs to despite the rating noise. Using these items the cluster-based bandit algorithm is able to efficiently adapt to user responses and rapidly learn the correct cluster to assign to a new user.